Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for optimizing a data protection service provider, comprising: analyzing, by the data protection service provider running on a computer of a target site, a first dataset associated with a first service provided by the data protection service provider, the first dataset comprising components from client data of a source site, for respective essentialness of the components of the first dataset; configuring, by the data protection service provider, a policy corresponding to the first service, the policy dictating when to replicate the respective components of the first dataset from the source site to the target site based on the respective essentialness of the components of the first dataset from the analyzing in order to minimize amount of data transfer from the source site to the target site in providing the first service for the client data; replicating, from the source site to the target site, the respective components of the first dataset according to the policy from the analyzing, upon detecting inputs enabling the first service, wherein the first service is a disaster recovery service for a database virtual machine of the source site, wherein the first dataset comprises components of a data volume, a log volume, and an index volume of the database virtual machine, wherein the data volume and the log volume are, from the analyzing, respectively determined to be essential for the disaster recovery service and the policy accordingly dictates that the data protection service provider replicates the data volume and the log volume from the source site to the target site, with a highest priority and most frequently, and wherein the index volume is determined to be valuable for the disaster recovery service and the policy accordingly dictates that the data protection service provider does not replicate the index volume from the source site to the target site when network bandwidth is not available but the data protection service provider reconstructs the index volume by use of a replicated data volume and a replicated log volume at the target site; and performing the disaster recovery service in the target site by use of the replicated data volume, the replicated log volume, and a reconstructed index volume.
The method optimizes data transfer for a disaster recovery service provided by a data protection service running on a computer at a target site. The service analyzes a dataset from a source site, which includes components of a database virtual machine such as a data volume, log volume, and index volume. The analysis determines the essentialness of each component for disaster recovery. Based on this analysis, a policy is configured to dictate replication priorities: the data and log volumes, deemed essential, are replicated with highest priority and frequency, while the index volume, deemed valuable but not essential, is only replicated when network bandwidth is available. If bandwidth is unavailable, the index volume is reconstructed at the target site using the replicated data and log volumes. This approach minimizes data transfer while ensuring critical components are available for disaster recovery. The method ensures efficient use of network resources by prioritizing essential data and dynamically adjusting replication based on availability.
2. The method of claim 1 , the analyzing comprising: determining that a first component of the first dataset is essential to the first service based on applying analytics data to the first dataset, wherein the first component being essential indicating that the first component is necessary for the target site to provide the first service, and wherein the policy for the first component that is essential dictates the data protection service provider to replicate the first component with a highest priority amongst all components of the first dataset.
This invention relates to data protection systems that prioritize the replication of essential components in a dataset to ensure uninterrupted service delivery. The problem addressed is the inefficient handling of data replication in backup or disaster recovery systems, where all components of a dataset are treated equally, leading to potential service disruptions if critical components are not prioritized. The method involves analyzing a dataset associated with a service to identify essential components that are necessary for the service to function. This analysis uses analytics data applied to the dataset to determine which components are critical. Once identified, these essential components are assigned the highest replication priority, ensuring they are replicated before other, less critical components. This prioritization improves the reliability and availability of the service by guaranteeing that the most important data is protected first. The system may also include additional steps such as monitoring the dataset for changes, dynamically adjusting replication priorities based on real-time analytics, and enforcing data protection policies that dictate how essential components should be handled. By focusing on the most critical data, the method optimizes resource usage and reduces the risk of service downtime.
3. The method of claim 1 , the analyzing comprising: determining that a second component of the first dataset is valuable to the first service based on applying analytics data to the first dataset, the second component being valuable indicating that the first service can be provided without replicating the second component to the target site, but efficiency of the first service at the target site would improve if the second component is replicated to the target site instead of being reconstructed from other replicated components of the first dataset, and wherein the policy for the second component that is valuable dictates the data protection service provider to replicate the second component when network bandwidth between the source site and the target site is available.
This invention relates to optimizing data replication in distributed systems, particularly for improving the efficiency of services that rely on datasets distributed across multiple sites. The problem addressed is the inefficient use of network bandwidth and computational resources when replicating datasets, where some components may not need full replication but could still benefit from selective replication to enhance service performance. The method involves analyzing a first dataset to determine the value of its components to a first service. A second component of the dataset is identified as valuable if it can be used by the service without replication but would improve efficiency if replicated rather than reconstructed from other replicated components. The analysis uses analytics data applied to the dataset to make this determination. For valuable components, a policy is applied that dictates replication only when network bandwidth between the source and target sites is available, ensuring efficient use of resources while maintaining service performance. This approach reduces unnecessary data transfers and computational overhead, optimizing both bandwidth and processing efficiency in distributed systems.
4. The method of claim 1 , the analyzing comprising: determining that a third component of the first dataset is non-essential to the first service based on applying analytics data to the first dataset, the third component being non-essential indicating that a transfer of the third component does not impact the first service at the target site, and wherein the policy for the third component that is non-essential dictates the data protection service provider not to replicate the third component to the target site.
This invention relates to data management systems that optimize data replication for services deployed across multiple sites. The problem addressed is inefficient data transfer, where unnecessary data components are replicated to target sites, consuming bandwidth and storage resources without benefiting the service. The solution involves analyzing datasets associated with a service to identify non-essential components that do not impact the service's functionality at the target site. Analytics data is applied to the dataset to determine which components are non-essential, meaning their transfer does not affect the service's operation. A policy is then applied to these non-essential components, instructing the data protection service provider to exclude them from replication. This ensures only essential data is transferred, reducing resource usage and improving efficiency. The method may also involve similar analysis for other datasets or services, ensuring comprehensive optimization across the system. The approach is particularly useful in distributed systems where minimizing data transfer is critical for performance and cost savings.
5. The method of claim 1 , wherein the first service is selected from a group consisting of a disaster recovery service and at least one value-added service available from the data protection service provider as selected by the client, wherein the at least one value-added service comprising a disaster recovery test service, a virus scanning service, an eDiscovery service, a compressibility estimation service.
This invention relates to data protection services, specifically methods for selecting and implementing additional services provided by a data protection service provider. The problem addressed is the need for clients to access multiple value-added services beyond basic data protection, such as disaster recovery, testing, security, and compliance, in a streamlined manner. The method involves selecting a first service from a predefined group, which includes a disaster recovery service and at least one additional value-added service. These value-added services may include a disaster recovery test service to verify backup integrity, a virus scanning service to detect malware in stored data, an eDiscovery service to assist in legal investigations, and a compressibility estimation service to optimize storage efficiency. The selection is made by the client based on their specific needs, and the chosen services are integrated into the data protection workflow. The method ensures that clients can customize their data protection solutions by adding specialized services that enhance functionality, security, and compliance. This approach simplifies service management by consolidating multiple capabilities under a single provider, reducing complexity and improving efficiency. The invention is particularly useful for enterprises requiring robust data protection with additional features tailored to their operational requirements.
6. The method of claim 1 , further comprising: obtaining, prior to the analyzing, analytics data associated with the first service, wherein the analytics data associated with the first service includes information used for assessing degrees of essentialness for the respective components of the first dataset in performing the first service.
This invention relates to a method for analyzing datasets used in service operations, particularly focusing on assessing the essentialness of components within a dataset for performing a service. The method addresses the challenge of determining which components of a dataset are critical to the functionality of a service, enabling more efficient data management and optimization. The method involves obtaining analytics data associated with a service before analyzing the dataset. This analytics data includes information that helps evaluate the importance or essentialness of individual components within the dataset for executing the service. By analyzing this data, the method can identify which components are most critical, allowing for targeted improvements, resource allocation, or redundancy planning. The analytics data may include metrics such as usage frequency, dependency relationships, or performance impact, which collectively inform the assessment of component essentialness. This approach ensures that the service remains robust and efficient by prioritizing the most critical dataset components.
7. The method of claim 6 , wherein the analytics data associated with the first service is selected from the group consisting of IT characteristics of virtual machines, storage volumes, and application programs involved in rendering the first service for the client data.
This invention relates to a system for managing and analyzing service delivery in a computing environment, particularly focusing on the performance and characteristics of services rendered to clients. The method involves collecting and analyzing analytics data associated with a first service to determine its operational efficiency and resource utilization. The analytics data includes IT characteristics of virtual machines, storage volumes, and application programs involved in delivering the service. By examining these components, the system can identify performance bottlenecks, optimize resource allocation, and ensure reliable service delivery. The method may also involve comparing the analytics data against predefined thresholds or historical data to detect anomalies or inefficiencies. This approach helps in maintaining service quality, reducing downtime, and improving overall system performance. The system can be applied in cloud computing, data centers, or enterprise IT environments where service reliability and efficiency are critical. The invention addresses the challenge of monitoring and optimizing complex service delivery infrastructures by providing detailed insights into the underlying IT components that support the service.
8. The method of claim 6 , wherein the analytics data associated with the first service is selected from the group consisting of user inputs, respective metadata for existing application programs, best practice patterns, and respective data requirements specific to each services for the client data.
This invention relates to a method for analyzing and optimizing service configurations in a computing environment. The method addresses the challenge of efficiently managing and customizing services based on client-specific data and requirements. The method involves collecting and processing analytics data associated with a first service to determine optimal configurations for other services. The analytics data includes user inputs, metadata from existing application programs, best practice patterns, and specific data requirements for each service. This data is used to generate configuration recommendations tailored to the client's needs, improving service performance and user experience. The method ensures that service configurations are dynamically adjusted based on real-time analytics, reducing manual intervention and enhancing system efficiency. By leveraging diverse data sources, the method provides a comprehensive approach to service optimization, ensuring alignment with best practices and client-specific requirements. The invention aims to streamline service management, minimize errors, and improve overall system reliability.
9. The method of claim 1 , the analyzing comprising: determining that each component of the first dataset has an essentialness score equal to one of {essential, valuable, non-essential} with respect to the first service, wherein a first component of the first dataset being essential indicates that the first component is necessary to provide the first service at the target site, and the policy for any essential component dictates the data protection service provider to replicate the essential component with a highest priority amongst all components of the first dataset, wherein a second component of the first dataset being valuable indicates that the second component can be reconstructed from essential components of the first dataset for the first service but the first service would be more efficient if the second component is replicated to the target site, and the policy for any valuable component dictates the data protection service provider to replicate the valuable component when network bandwidth between the source site and the target site is available, and wherein a third component of the first dataset being non-essential indicates that a transfer of the third component to the target site does not impact the first service at the target site, and the policy for any non-essential component dictates the data protection service provider not to replicate the non-essential component to the target site.
A system and method for prioritizing data replication in a distributed service environment analyzes components of a dataset to determine their essentialness for a service at a target site. The system categorizes each component into one of three tiers: essential, valuable, or non-essential. Essential components are necessary for the service to function at the target site and are replicated with the highest priority. Valuable components can be reconstructed from essential components but improve service efficiency if replicated, so they are transferred only when network bandwidth is available. Non-essential components do not affect service performance and are excluded from replication. The system applies predefined policies to enforce these replication rules, ensuring optimal resource utilization while maintaining service availability. This approach improves data transfer efficiency by prioritizing critical data and avoiding unnecessary transfers, particularly in bandwidth-constrained environments. The method dynamically adjusts replication based on component importance, reducing latency and ensuring service reliability.
10. A computer program product comprising: a computer readable storage medium readable by one or more processor and storing instructions for execution by the one or more processor for performing a method for optimizing a data protection service provider, comprising: analyzing, by the data protection service provider running on a computer of a target site, a first dataset associated with a first service provided by the data protection service provider, the first dataset comprising components from client data of a source site, such that the data protection service provider determines a policy corresponding to the first service, the policy dictating when and how to replicate the respective components of the first dataset from the source site to the target site in order to minimize cost of providing the first service for the source site, wherein the cost comprises network bandwidth and storage footprint, the network bandwidth being to replicate the first dataset from the source site to the target site, and the storage footprint being to maintain the first dataset in the target site; replicating, from the source site to the target site, the respective components of the first dataset according to the policy from the analyzing, upon detecting inputs enabling the first service, wherein the first service is a virus scanning service for a database virtual machine of the source site, wherein the first dataset comprises a data volume, a log volume, and an index volume of the database virtual machine, and wherein the data volume, the log volume, and the index volume are, from the analyzing, respectively determined to be essential for the virus scanning service and the policy accordingly dictates that the data protection service provider replicates the data volume, the log volume, and the index volume from the source site to the target site with a highest priority and most frequently; and performing the virus scanning service in the target site by use of the replicated data volume, the replicated log volume, and the replicated index volume.
This invention relates to optimizing data protection services, specifically for virus scanning of database virtual machines. The system analyzes a dataset associated with a virus scanning service, which includes components like data volumes, log volumes, and index volumes from a source site's database virtual machine. The analysis determines a policy that dictates how and when to replicate these components to a target site to minimize costs, including network bandwidth and storage footprint. The policy prioritizes replication of essential components—such as data, log, and index volumes—with the highest priority and frequency for efficient virus scanning. Upon detecting inputs that enable the service, the system replicates these components from the source to the target site according to the policy. The virus scanning service is then performed at the target site using the replicated volumes. This approach ensures that critical data is available for scanning while optimizing resource usage.
11. The computer program product of claim 10 , the analyzing comprising: determining that a first component of the first dataset is essential to the first service based on applying analytics data to the first dataset, the first component being essential indicating that the first component is necessary for the target site to provide the first service, such that the policy is determined for the data protection service provider to replicate the first component with a highest priority and most frequently.
This invention relates to data protection and replication systems, specifically for prioritizing the replication of essential components in a dataset to ensure service continuity. The problem addressed is the inefficient or non-prioritized replication of data components, which can lead to service disruptions if critical components are not replicated promptly. The system analyzes a dataset associated with a service to identify essential components that are necessary for the target site to provide the service. This analysis involves applying analytics data to the dataset to determine which components are critical. Once identified, these essential components are replicated with the highest priority and most frequently by the data protection service provider. This ensures that the most important data is always available, minimizing downtime and maintaining service reliability. The invention also includes determining a policy for the data protection service provider, which dictates the replication strategy based on the identified essential components. This policy ensures that replication resources are allocated optimally, focusing on the most critical data first. The system may also involve monitoring the dataset and adjusting the replication strategy dynamically as the importance of components changes over time. This adaptive approach ensures that the replication process remains efficient and aligned with the current needs of the service.
12. The computer program product of claim 10 , the analyzing comprising: determining that a second component of the first dataset is valuable to the first service based on applying analytics data to the first dataset, the second component being valuable indicating that the second component is not necessary but helpful for the target site to provide the first service, such that the policy is determined for the data protection service provider to replicate the second component if the network bandwidth is available.
This invention relates to data management and protection in distributed computing environments, specifically optimizing data replication based on value and network conditions. The system evaluates datasets to identify components that are valuable but not strictly necessary for a target service, enabling selective replication when network bandwidth is available. The process involves analyzing a first dataset associated with a first service to determine the value of its components. A second component is identified as valuable if it enhances the service but is not essential, allowing the system to prioritize replication of this component when sufficient network bandwidth exists. This approach ensures efficient use of network resources while maintaining service quality. The invention also includes determining a policy for a data protection service provider to replicate the valuable component under favorable network conditions, improving data availability without unnecessary bandwidth consumption. The system dynamically adjusts replication decisions based on real-time analytics and network status, optimizing performance for distributed services.
13. The computer program product of claim 10 , the analyzing comprising: determining that a third component of the first dataset is non-essential to the first service based on applying analytics data to the first dataset, the third component being non-essential indicating that the third component does not contribute to the first service when present at the target site, such that the policy is determined for the data protection service provider not to replicate the third component to the target site.
This invention relates to data management in distributed systems, specifically optimizing data replication for a service by identifying and excluding non-essential components. The problem addressed is inefficient data replication, where unnecessary components are transferred to target sites, consuming bandwidth and storage resources without contributing to the service's functionality. The system analyzes a dataset associated with a service to determine which components are essential for the service's operation at a target site. Analytics data is applied to the dataset to identify non-essential components—those that do not contribute to the service when present at the target site. Once identified, these components are excluded from replication, reducing the data volume transferred and stored. The system generates a policy for a data protection service provider, instructing it to avoid replicating the non-essential components to the target site. This approach improves efficiency by minimizing redundant data transfers, optimizing storage usage, and reducing network load. The solution is particularly useful in environments where data replication is frequent, such as disaster recovery or distributed computing systems, where unnecessary data can significantly impact performance and costs. The method ensures that only relevant data is replicated, enhancing system reliability and resource utilization.
14. A system comprising: a memory; one or more processor in communication with the memory; and program instructions executable by the one or more processor via the memory to perform a method for optimizing a data protection service provider, comprising: analyzing, by the data protection service provider running on a computer of a target site, a first dataset associated with a first service provided by the data protection service provider, the first dataset comprising components from client data of a source site, such that the data protection service provider determines a policy corresponding to the first service, the policy dictating when and how to replicate the respective components of the first dataset from the source site to the target site in order to minimize cost of providing the first service for the source site, wherein the cost comprises network bandwidth and storage footprint, the network bandwidth being to replicate the first dataset from the source site to the target site, and the storage footprint being to maintain the first dataset in the target site; replicating, from the source site to the target site, the respective components of the first dataset according to the policy from the analyzing, upon detecting inputs enabling the first service, wherein the first service is a disaster recovery service for a database virtual machine of the source site, wherein the first dataset comprises components of a data volume, a log volume, and an index volume of the database virtual machine, wherein the data volume and the log volume are, from the analyzing, respectively determined to be essential for the disaster recovery service and the policy accordingly dictates that the data protection service provider replicates the data volume and the log volume from the source site to the target site, with a highest priority and most frequently, and wherein the index volume is determined to be valuable for the disaster recovery service and the policy accordingly dictates that the data protection service provider does not replicate the index volume from the source site to the target site when the network bandwidth is not available but the data protection service provider reconstructs the index volume by use of a replicated data volume and a replicated log volume at the target site; and performing the disaster recovery service in the target site by use of the replicated data volume, the replicated log volume, and a reconstructed index volume.
The system optimizes data protection services, specifically disaster recovery for database virtual machines, by intelligently managing replication to minimize costs. The system analyzes a dataset from a source site, which includes components like data volumes, log volumes, and index volumes of a database virtual machine. Based on this analysis, it determines a policy that dictates how and when to replicate these components to a target site, prioritizing essential components (e.g., data and log volumes) for frequent, high-priority replication while deferring or reconstructing less critical components (e.g., index volumes) when network bandwidth is constrained. The policy aims to balance network bandwidth usage and storage footprint, ensuring efficient disaster recovery while reducing operational costs. Upon detecting inputs that trigger the disaster recovery service, the system replicates the prioritized components and reconstructs the index volume at the target site using the replicated data and log volumes. This approach ensures that critical data is always available for recovery while optimizing resource usage.
15. The system of claim 14 , the analyzing comprising: determining that a first component of the first dataset is essential to the first service based on applying analytics data to the first dataset, the first component being essential indicating that the first component is necessary for the target site to provide the first service, such that the policy is determined for the data protection service provider to replicate the first component with a highest priority and most frequently.
This invention relates to a system for managing data protection services, particularly in environments where certain data components are critical to maintaining essential services. The problem addressed is the inefficient prioritization of data replication in distributed systems, where not all data components are equally important for service continuity. The system identifies essential components within a dataset that are necessary for a target site to provide a specified service. By analyzing the dataset using analytics data, the system determines which components are critical and assigns them the highest replication priority. These essential components are then replicated more frequently than non-essential data, ensuring that critical services remain operational even in the event of data loss or system failures. The system dynamically adjusts replication priorities based on the evolving importance of data components, optimizing resource usage while maintaining service reliability. This approach is particularly useful in cloud computing, disaster recovery, and distributed database systems where minimizing downtime is critical. The invention improves upon prior art by introducing a data-driven method for prioritizing replication, reducing unnecessary replication overhead and ensuring that the most critical data is always available.
16. The system of claim 14 , the analyzing comprising: determining that a second component of the first dataset is valuable to the first service based on applying analytics data to the first dataset, the second component being valuable indicating that the second component is not necessary but helpful for the target site to provide the first service, such that the policy is determined for the data protection service provider to replicate the second component if the network bandwidth is available.
A system analyzes datasets to determine the value of components for a service, particularly in scenarios where network bandwidth is limited. The system evaluates a first dataset associated with a first service to identify components that are valuable but not strictly necessary for the service. This determination is made by applying analytics data to the dataset, where "valuable" means the component enhances the service without being essential. The system then generates a policy for a data protection service provider, instructing replication of the valuable component only if sufficient network bandwidth is available. This approach optimizes data transfer by prioritizing critical components while conditionally replicating helpful but non-essential data when resources permit. The system ensures efficient use of network resources while maintaining service quality. The policy dynamically adjusts based on available bandwidth, ensuring that valuable but non-critical data is replicated only when feasible. This method is particularly useful in environments where network conditions vary, such as cloud-based or distributed systems. The system may also include additional components for monitoring network conditions and adjusting replication policies accordingly.
17. The system of claim 14 , the analyzing comprising: determining that a third component of the first dataset is non-essential to the first service based on applying analytics data to the first dataset, the third component being non-essential indicating that the third component does not contribute to the first service when present at the target site, such that the policy is determined for the data protection service provider not to replicate the third component to the target site.
This invention relates to data management systems that optimize data replication for services deployed at target sites. The problem addressed is inefficient data replication, where unnecessary or non-essential data components are unnecessarily transferred to target sites, consuming bandwidth and storage resources without contributing to the service's functionality. The system analyzes datasets associated with a service to identify non-essential components that do not contribute to the service's operation when present at the target site. This analysis involves applying analytics data to the dataset to determine which components are redundant or irrelevant. Once identified, a policy is generated for a data protection service provider to exclude these non-essential components from replication, ensuring only essential data is transferred. This reduces storage and bandwidth usage while maintaining service functionality. The system may also include a data protection service provider that enforces the replication policy, ensuring only essential data is replicated to the target site. The analysis may further involve comparing the dataset against historical or reference data to identify non-essential components. The system may also track changes in the dataset over time to dynamically adjust the policy as the service's requirements evolve. This approach optimizes data replication efficiency while ensuring service reliability.
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February 25, 2020
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